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With the continuous development of the Internet of Things (IoT) technology, the industry’s awareness of the security of the IoT is also increasing, and the adoption of quantum communication technology can significantly improve the communication security of various devices in the IoT. This paper proposes a scheme of controlled remote quantum state preparation and quantum teleportation based on multiple communication parties, and a nine-qubit entanglement channel is used to achieve secure communication of multiple devices in the IoT. The channel preparation, measurement operation, and unitary operation of the scheme were successfully simulated on the IBM Quantum platform, and the entanglement degree and reliability of the channel were verified through 8192 shots. The scheme’s application in the IoT was analyzed, and the steps and examples of the scheme in the secure communication of multiple devices in the IoT are discussed. By simulating two different attack modes, the effect of the attack on the communication scheme in the IoT was deduced, and the scheme’s high security and anti-interference ability was analyzed. Compared with other schemes from the two aspects of principle and transmission efficiency, it is highlighted that the advantages of the proposed scheme are that it overcomes the single fixed one-way or two-way transmission protocol form of quantum teleportation in the past and can realize quantum communication with multiple devices, ensuring both security and transmission efficiency.

As smartphones and other small portable devices become more sophisticated and popular, opportunities for communication and information sharing among such device users have increased. In particular, since it is known that infrastructure-less device-to-device (D2D) communication platforms consisting only of such devices are excellent in terms of, for example, bandwidth efficiency, efforts are being made to merge their information sharing capabilities with conventional infrastructure. However, efficient multi-hop communication is difficult with the D2D communication protocol, and many conventional D2D communication platforms require modifications of the protocol and terminal operating systems (OSs). In response to these issues, this paper reports on a proposed tree-structured D2D communication platform for Android devices that combines Wi-Fi Direct and Wi-Fi functions. The proposed platform, which is expected to be used with general Android 4.0 (or higher) OS equipped terminals, makes it possible to construct an ad hoc network instantaneously without sharing prior knowledge among participating devices. We will show the feasibility of our proposed platform through its design and demonstrate the implementation of a prototype using real devices. In addition, we will report on our investigation into communication delays and stability based on the number of hops and on terminal performance through experimental confirmation experiments.

Driven by the global energy transition and carbon-neutrality goals, virtual power plants (VPPs) are expected to aggregate distributed energy resources and participate in multiple electricity markets while achieving economic efficiency and low carbon emissions. However, the strong volatility of wind and photovoltaic generation, together with the coupling between electric and thermal loads, makes real-time VPP scheduling challenging. Existing deep reinforcement learning (DRL)-based methods still suffer from limited predictive awareness and insufficient handling of physical and carbon-related constraints. To address these issues, this paper proposes an improved model, termed SAC-LAx, based on the Soft Actor–Critic (SAC) deep reinforcement learning algorithm for intelligent VPP scheduling. The model integrates an Attention–xLSTM prediction module and a Linear Programming (LP) constraint module: the former performs multi-step forecasting of loads and renewable generation to construct an extended state representation, while the latter projects raw DRL actions onto a feasible set that satisfies device operating limits, energy balance, and carbon trading constraints. These two modules work together with the SAC algorithm to form a closed perception–prediction–decision–control loop. A campus integrated-energy virtual power plant is adopted as the case study. The system consists of a gas–steam combined-cycle power plant (CCPP), battery storage, a heat pump, a thermal storage unit, wind turbines, photovoltaic arrays, and a carbon trading mechanism. Comparative simulation results show that, at the forecasting level, the Attention–xLSTM (Ax) module reduces the day-ahead electric load Mean Absolute Percentage Error (MAPE) from 4.51% and 5.77% obtained by classical Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to 2.88%, significantly improving prediction accuracy. At the scheduling level, the SAC-LAx model achieves an average reward of approximately 1440 and converges within around 2500 training episodes, outperforming other DRL algorithms such as Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Proximal Policy Optimization (PPO). Under the SAC-LAx framework, the daily net operating cost of the VPP is markedly reduced. With the carbon trading mechanism, the total carbon emission cost decreases by about 49% compared with the no-trading scenario, while electric–thermal power balance is maintained. These results indicate that integrating prediction enhancement and LP-based safety constraints with deep reinforcement learning provides a feasible pathway for low-carbon intelligent scheduling of VPPs.

The traditional weighing and selling process of non-barcode items requires manual service, which not only consumes manpower and material resources but is also more prone to errors or omissions of data. This paper proposes an intelligent self-service vending system embedded with a single camera to detect multiple products in real-time performance without any labels, and the system realizes the integration of weighing, identification, and online settlement in the process of non-barcode items. The system includes a self-service vending device and a multi-device data management platform. The flexible configuration of the structure gives the system the possibility of identifying fruits from multiple angles. The height of the system can be adjusted to provide self-service for people of different heights; then, deep learning skill is applied implementing product detection, and real-time multi-object detection technology is utilized in the image-based checkout system. In addition, on the multi-device data management platform, the information docking between embedded devices, WeChat applets, Alipay, and the database platform can be implemented. We conducted experiments to verify the accuracy of the measurement. The experimental results demonstrate that the correlation coefficient R2 between the measured value of the weight and the actual value is 0.99, and the accuracy of non-barcode item prediction is 93.73%. In Yangpu District, Shanghai, a comprehensive application scenario experiment was also conducted, proving that our system can effectively deal with the challenges of various sales situations.

Semantic segmentation of ultra-high-resolution remote sensing (UHR-RS) imagery plays a critical role in land use and land cover analysis, yet it remains computationally intensive due to the enormous input size and high spatial complexity. Existing studies have commonly employed strategies such as patch-wise processing, multi-scale model architectures, lightweight networks, and representation sparsification to reduce resource demands, but they have often struggled to maintain long-range contextual awareness and scalability for inputs of arbitrary size. To address this, we propose RingFormer-Seg, a scalable Vision Transformer framework that enables long-range context learning through multi-device parallelism in UHR-RS image segmentation. RingFormer-Seg decomposes the input into spatial subregions and processes them through a distributed three-stage pipeline. First, the Saliency-Aware Token Filter (STF) selects informative tokens to reduce redundancy. Next, the Efficient Local Context Module (ELCM) enhances intra-region features via memory-efficient attention. Finally, the Cross-Device Context Router (CDCR) exchanges token-level information across devices to capture global dependencies. Fine-grained detail is preserved through the residual integration of unselected tokens, and a hierarchical decoder generates high-resolution segmentation outputs. We conducted extensive experiments on three benchmarks covering UHR-RS images from 2048 × 2048 to 8192 × 8192 pixels. Results show that our framework achieves top segmentation accuracy while significantly improving computational efficiency across the DeepGlobe, Wuhan, and Guangdong datasets. RingFormer-Seg offers a versatile solution for UHR-RS image segmentation and demonstrates potential for practical deployment in nationwide land cover mapping, supporting informed decision-making in land resource management, environmental policy planning, and sustainable development.

Television viewers interacting with second screens has become a common sight in the modern living room. Such activities are a mixture of related, semi-related, and non-related browsing of content. This growing trend is revolutionising the way that broadcasters think about their content. Through the envisioned connected home, driven by end-to-end IP connected networks, television content creators and app developers are now considering the design space for multi-device, interactive experiences. In this review paper, we consider the pre-digital beginnings of such scenarios and progress to discuss how the introduction of mobile devices has affected the TV viewing experience. We discuss dual-screen usage over a variety of contexts in the connected home, with a focus on ‘designed’ dual-screen experiences such as companion applications. We conclude with reflections on the future of this area so that app developers, broadcasters, and academics may push further the space and improve future dual- and multi-screen experiences.

Device-to-Multi-Device (D2MD) communication shows great advantages in maximizing the offloading of Base Station (BS). At present, the main research works are based on mathematical model optimization methods, viz, spatial distribution model and content request model of Content Request Users (CRUs), and social relationship intensity model, which can estimate the D2MD transmission performance in order to reduce the BS traffic to a greater extent by optimizing the D2MD user grouping. However, for the real-world system, the three models are complex and difficult to describe in realistic scenario; for Seed Users (SUs), they have inherent selfishness, i.e., they want to communicate with lower transmission power; for CRU, they want to obtain the content in the shortest possible waiting time. Hence, the problem that how to reduce the BS traffic by D2MD user grouping and incentive SUs to contribute transmission power becomes very difficult to solve. In order to solve it, we describe the D2MD user grouping with transmission power control processes in this scenario, and then model the problem as a joint problem of game and long-term optimization. Then, we use matching-Stackelberg hierarchical game and Q-learning algorithm to solve it. Specially, at first, we propose a matching and incentive-based power control method, which maximize the myopic offloading of BS with lower transmission power of SUs, i.e., higher energy efficiency; Secondly, we design a D2MD user grouping algorithm based on multi-agent deep Q-learning algorithm, in order to maximize the long-term average offloading of BS. Finally, the results of simulation experiments show that the proposed algorithm can maximize the long-term average offloading of BS while access more CRUs with quality of service-guaranteed, and it can maximize the energy efficiency of SUs as well.

Impedance spectroscopy is a powerful experimental technique for complex analyses of electrical response of conducting samples. Experimental setups consist of a wide variety of analyzers and temperature controllers for high and low temperatures, and the experiments may last for days. In this paper, a self-developed controlling software written in C++ and working under GNU Linux that co-operates with many different devices in the laboratory is described. Measurements can be carried out at elevated and low temperatures, using either a furnace or a cryostat, i.e., above 77 K, and to a temperature exceeding 1000 K. The major advantage of this solution is the possibility to freely combine different analyzers and temperature controllers according to actual experimental needs. The possibility to conduct several parallel measurements at one PC makes this software interesting for every well-equipped and intensively used laboratory of impedance spectroscopy.

At present, Multi-Devices-Process Integrated Scheduling Algorithm with Time-Selective Strategy for Process Sequence (MISATPS) is an advanced algorithm in the field of integrated scheduling with multi-devices-process problems. This algorithm ignores the influence of the pre-process on the post-process when solving the multi-devices-process integrated scheduling problem, which leads to the problem of poor closeness between serial processes and poor parallelism between parallel processes. This paper points out that there is no restriction of scheduling sequence between parallel processes on the same processing device. It can be scheduled flexibly of the sequence between parallel processes of the same device. Therefore, based on the scheduling scheme of MISATPS, the algorithm is improved by applying the interchange strategy and the interchange adjustment strategy of multi-device adjacent parallel process. In this way, the influence of the pre-process on the post-process is avoided, the compactness of the serial process and the parallelism of the parallel process are improved, and the scheduling result is optimized.

With the advancement of Industry 4.0, 3D printing has become a critical technology in smart manufacturing; however, challenges remain in the integrated management, quality control, and remote monitoring of multiple 3D printers. This study proposes an intelligent cloud monitoring system based on the SharkNet dynamic network, IoT, and artificial neural networks (ANNs). The system utilizes a SharkNet dynamic network to integrate low-cost sensors for environmental monitoring to enable low-latency data transmission and deploys ANN models on the cloud for print quality prediction and process parameter optimization. Next, we experimentally validated the system using the Taguchi design and ANN-based analysis, focusing on optimizing printing process parameters and improving surface quality. The main results show that the designed system has a communication delay of 40–50 ms and 99.8% transmission reliability under moderate load, and the system reduces the surface roughness prediction error to less than 17.2%. In addition, the ANN model outperforms conventional methods in capturing the nonlinear relationships of the variables, and the system can be based on the model to improve print quality and productivity by enabling real-time parameter adjustments. The system retains a high degree of scalability in terms of real-time monitoring and parallel or complex control of multiple devices, which demonstrates its potential for applications in smart manufacturing.